Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

Power Quality Improvement of Distributed Generation Integrated Network with Unified Power Quality Conditioner.

With the increased penetration of small scale renewable energy sources in the electrical distribution network, maintenance or improvement of power quality has become more critical than ever where the level of voltage and current harmonics or disturbances can vary widely. For this reason, Custom Power Devices (CPDs) such as the Unified Power Quality Conditioner (UPQC) can be the most appropriate solution for enhancing the dynamic performance of the distribution network, where accurate prior knowledge may not be available. Therefore, the main objective of the present research is to investigate the (i) placement (ii) integration (iii) capacity enhancement and (iv) real time control of the Unified Power Quality Conditioner (UPQC) to improve the power quality (PQ) of a distributed generation (DG) network connected to the grid or microgrid

Development of a Converter-Based Testing Platform and Battery Energy Storage System (BESS) Emulator for Microgrid Controller Function Evaluation

The microgrid has attracted increasing research attention in the last two decades. Due to the development of renewable energy resources and power electronics technologies, the future microgrid will trend to be smarter and more complicated. The microgrid controller performs a critical role in the microgrid operation, which will also become more and more sophisticated to support the future microgrid. Before final field deployment and test, the evaluation and testing of the controller is an indispensable step in the controller development, which requires a proper testing platform. However, existing simulation-based platforms have issues with potential numerical oscillation and may require huge computation resources for complex microgrid controllers. Meanwhile, field test-based controller evaluation is limited to the test conditions. Existing digital simulation-based platforms and field test-based platforms have limitations for microgrid controller testing. To provide a practical and flexible controller evaluation, a converter-based microgrid hardware testbed is designed and implemented considering the actual microgrid architecture and topology information. Compared with the digital simulation-based platforms, the developed microgrid testing platform can provide a more practical testing environment. Compared to the direct field test, the developed platform is more flexible to emulate different microgrids. As one of the key components, a converter-based battery energy storage system (BESS) emulator is proposed to complete the developed testing platform based on the testing requirements of microgrid controller functions. Meanwhile, the microgrid controller testing under different microgrid conditions is also considered. Two controllers for the microgrid with dynamic boundaries are tested to demonstrate the capability of the developed platform as well as the BESS emulator. Different testing cases are designed and tested to evaluate the controller performance under different microgrid conditions

A robust islanding detection method with zero-non-detection zone for distribution systems with DG

This paper proposes a strategy for detecting unintentional islanding operations (IOs) in distribution networks (DNs) with distributed generation (DG), which eliminating the non-detection zone (NDZ). This hybrid method achieves a zero-NDZ by taking advantage of both passive and active methodologies for an inverter-based DG scenario. The passive-based part of the proposed method considers settings with low thresholds and is activated whenever they are surpassed. The following step uses a three-phase static RC load. This load is connected to intentionally force the frequency and its derivative to exceed the established thresholds. Thus, the events with zero power imbalance can be identified. Unlike other existing methods, this technique does not degrade the power quality (PQ) and does not require DG output power curtailment. The evaluation of the proposed strategy has been carried out through an extensive set of scenarios considering both islanding and non-islanding events. The islanding detection capabilities of the proposed method have been explored considering a custom-made DN test system and the test system recommended by the IEEE 929-2000 standard. The proposed method has a simple implementation, requires a low level of computational complexity, provides a high degree of reliability, and assures fast islanding detection.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energèticaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesPostprint (published version

Islanding Detection and Power Quality Analysis in Microgrid

芝浦工業大学2019年